Senior interview questionAsked at LinkedIn · interview write-up
Implement a local training pipeline for a text-embedding recommender built from member profiles, item text and click or skip interactions, with leakage-resistant splits and reproducible inference.
Model answer
Join each impression to the profile as of that timestamp, label clicks positive and skips negative, then split by time: train on the past, validate on the next window, and keep anything fitted, like vocabularies or normalizers, to the training slice.
Model: a two-tower text encoder for member and item, trained contrastively with in-batch negatives plus skips as hard negatives. Evaluate recall at k against a popularity baseline.
For reproducibility, pin seeds, model versions and a data-snapshot hash in one config, ship preprocessing with the model, and test that inference reproduces the training embeddings.
Practise the rest
17 Embeddings questions, 492 in all, each with a model answer. Free.
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